Updated 10 Sep 2020
This study develops a novel metaheuristic algorithm that is inspired by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. The new algorithm is successfully tested on benchmark functions and optimization problems. Notably, JS has only two control parameters, which are population size and number of iterations. Therefore, JS is very simple to use, and potentially an excellent metaheuristic algorithm for solving optimization problems.
Chou, Jui-Sheng, and Dinh-Nhat Truong. “A Novel Metaheuristic Optimizer Inspired by Behavior of Jellyfish in Ocean.” Applied Mathematics and Computation, vol. 389, Elsevier BV, Jan. 2021, p. 125535, doi:10.1016/j.amc.2020.125535.
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